Package: EBMAforecast 1.0.32
EBMAforecast: Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs Sampling or EM-Algorithms
Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.
Authors:
EBMAforecast_1.0.32.tar.gz
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EBMAforecast_1.0.32.tgz(r-4.4-x86_64)EBMAforecast_1.0.32.tgz(r-4.4-arm64)EBMAforecast_1.0.32.tgz(r-4.3-x86_64)EBMAforecast_1.0.32.tgz(r-4.3-arm64)
EBMAforecast_1.0.32.tar.gz(r-4.5-noble)EBMAforecast_1.0.32.tar.gz(r-4.4-noble)
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EBMAforecast.pdf |EBMAforecast.html✨
EBMAforecast/json (API)
| # Install 'EBMAforecast' in R: |
| install.packages('EBMAforecast', repos = c('https://fhollenbach.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fhollenbach/ebma/issues
- calibrationSample - Sample data Insurgency Predictions
- presidentialForecast - Sample data Presidential Election
- testSample - Sample data Insurgency Predictions
Last updated 8 months agofrom:04118be6f8. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 16 2024 |
| R-4.5-win-x86_64 | OK | Oct 16 2024 |
| R-4.5-linux-x86_64 | OK | Oct 16 2024 |
| R-4.4-win-x86_64 | OK | Oct 16 2024 |
| R-4.4-mac-x86_64 | OK | Oct 16 2024 |
| R-4.4-mac-aarch64 | OK | Oct 16 2024 |
| R-4.3-win-x86_64 | OK | Oct 16 2024 |
| R-4.3-mac-x86_64 | OK | Oct 16 2024 |
| R-4.3-mac-aarch64 | OK | Oct 16 2024 |
Exports:calibrateEnsemblecompareModelsEBMApredictmakeForecastDataplotprintsetModelNames<-setOutcomeCalibration<-setOutcomeTest<-setPredCalibration<-setPredTest<-showsummary
Dependencies:abindbackportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigplyrR6rappdirsRColorBrewerRcpprlangrmarkdownrpartrstudioapisassscalesseparationplotstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calibrate an ensemble Bayesian Model Averaging model | calibrateEnsemble calibrateEnsemble,ForecastData-method |
| Sample data Insurgency Predictions | calibrationSample testSample |
| Function for comparing multiple models based on predictive performance | compareModels compareModels,ForecastData-method CompareModels-class |
| EBMApredict | EBMApredict EBMApredict,ForecastData-method |
| An ensemble forecasting data object | ForecastData-class |
| Build a ensemble forecasting data object | makeForecastData makeForecastData,ANY-method |
| Sample data Presidential Election | presidentialForecast |
| Print and Show methods for forecast data | print,ForecastData-method print,SummaryForecastData-method show,ForecastData-method show,SummaryForecastData-method |
| "Set" functions | setModelNames<- setModelNames<-,ForecastData-method setOutcomeCalibration<- setOutcomeCalibration<-,ForecastData-method setOutcomeTest<- setOutcomeTest<-,ForecastData-method setPredCalibration<- setPredCalibration<-,ForecastData-method setPredTest<- setPredTest<-,ForecastData-method |
| Summarize and Plot Ensemble models | plot,FDatFitLogit-method plot,FDatFitNormal-method summary,FDatFitLogit-method summary,FDatFitNormal-method SummaryForecastData-class |
